This research addresses the task of representing and recognizing events in a tactical domain from large-scale spatio-temporal data under conditions of limited observability and high noise with real-time response constraints. These assumptions differ from those traditionally made in plan recognition and produce a problem that combines aspects of plan recognition, pattern recognition and object tracking. This research provides evidence that parsimonious qualitative representations used to represent pair-wise interactions among agents can be combined to identify large-scale group behaviors that form the basis of increasingly complex patterns of activity.
A comprehensive software application was constructed to demonstrate the claims of the thesis by evaluating performance on a real-world problem involving the recognition of a tactical maneuver in actual US Army training battles. Evaluations were conducted and performance evaluated by both novices and active military subject matter experts.